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Journal of Civil Engineering, Science and Technology Volume 11, Issue 2, September 2020

SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO

LAND COVER TRANSFORMATION

Syed Riad Morshed, Md. Abdul Fattah, Asma Amin Rimi and Md. Nazmul Haque* Department of Urban and Regional Planning, Khulna University of Engineering & Technology, Khulna University of

Engineering & Technology, Khulna-9203, Bangladesh.

Date received: 26/04/2020 Date accepted: 20/08/2020

*Corresponding author’s email: nhaque.kuet13@gmail.com

DOI: 10.33736/jcest.2234.2020

Abstract — This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover

Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis

in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its

influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference

Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the

LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows

that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand,

the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in

temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in

Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is

the transformation of land cover in buildup areas.

Copyright © 2020 UNIMAS Publisher. This is an open access article distributed under the Creative Commons Attribution-Non-commercial-ShareAlike 4.0

International License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: Land Cover Change, LST Dynamics, NDBI, NDMI, Raster Based Approach

1.0 INTRODUCTION

The enormous population growth has resulted in the increase of new cities, industries, vehicles, and

infrastructures. Urbanization and industrialization have been influenced in the drastic change of land

cover types around the world. This has resulted in the extensive environmental changes around the world.

Global warming, changes in ecosystems, climate change, greenhouse emissions, increase of sea level

and many other problems are increasing day by day due to the massive transformation of land cover

types [1]. The study aimed to identify how significantly the Land Surface Temperature (LST) responses

to the change of Land Use Patterns (LUP) in some contiguous wards of a developing city. Environmental

changes such as temperature, rainfall, LST and so on does not changes equally in all portions of the city

because the development of change of land cover types do not changes equally in all directions. The city

shows the overall different changes on a large scale. The study focused on identifying the micro levels

(ward wise) change of LST and LUP of Khulna city. For these three adjacent wards, Ward No 9, Ward

No 14 and Ward No 16 were selected in this study.

In Growth of the population has been increasing the need for new housing, workplace, education, food,

and other basic needs along with the increase of human activities. Again, various activities are going on

to improve the living standard of the people, which is exerting a negative influence on Land Cover Type

(LCT). Human activities on a large scale are incessantly influencing in the massive transformation of

LCT [2]. A huge percentage of water body vacant land, agricultural land, vegetation, wetlands are

decreasing every day and transforming into buildup areas around the world. This massive Land Cover

Type Transformation (LUTT) affecting the Surface Energy Budget (SEB) through altering the climate.

One of the important parameters for estimating SEB is Land Surface Temperature (LST) through

assessing land use and land cover (LULC) pattern changes [3]. Various methods and approaches are

introduced by many researchers to measure and analyze the LST [4,5,6,7].

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Due to urbanization and unplanned development resulted in a malignant loss of greeneries, vacant space,

waterbodies, eco-system, and ecologically sensitive habitats [8]. Many houses, shopping malls,

buildings, infrastructures are being built in every year. As a result of the unplanned urbanization, the

sensitive areas are destroyed. People are building infrastructures wherever they wish, without thinking

about the mother nature. As a result, the environment is deteriorating its balance. The earth’s average

temperature and the concentration of CO2 in the air is increasing, but the total average rainfall is

decreasing. Since 1880, the global annual average temperature rose at an average rate of 0.07°C (0.13

°F) per decade [9]. According to Shahid [10], the mean temperature in Bangladesh has risen by

0.1030C/year during the period 1958-2007 and for Khulna the rate was 0.0070C/year during 1960-2012.

But with the due to massive environmental change the mean temperature increasing rate stood at

0.041620C/year in Khulna during 2003 to 2018 [11]. If the land use transformation continues in current

rate, within 2050 the temperature will rise to 10C in Khulna and there will be no land to transform into

build area.

Increase of buildup area reduces the heat reflection capacity of soil and soil starts to retain heat which

increases the surface temperature. Destruction of waterbodies like river, ponds, wetlands, canals etc. and

increase of buildup area prevents water from entering the deep soil. For which ground water level is

declining and soil moisture retention capacity is decreasing. influencing in the decrease of Normalized

Difference Moisture Index (NDMI) value [12] and rise of land surface temperature. Again, due to the

destruction of greeneries and vegetation areas, the value of Normalized Difference Vegetation Index

(NDVI) [13] decreases and increase of buildup area increases Normalized Difference Buildup Index

(NDBI) value [14]. These three factors response mostly with the change of land use pattern and

influences in the change of LST in any area and global LST rose from 0.60C to 0.90C (1.10F to 1.60F)

during the year 1906 to 2005[11,15].

Many researchers have worked on the change of LST in Dhaka City in the literature [16,17,18,19].

Among all these researches, [16] calculated all the factors and indexes related to the change of the LST.

Sharmin [20] showed the relationship between NDVI and LST. Kafy et. al. illustrated the land cover

change and LST change in the Rajshahi City Corporation area in the literature [28]. These all research

showed the influences of land use change in the change of Land surface temperature in a city or in a

division that means in a large scale. But, the change of LUP does not occur uniformly over the city. LUP

change is much higher in industrial areas of a city rather than in the residential areas and the vegetation

areas. Again, the surrounding areas of the industrial areas have higher environmental temperatures rather

than other areas of a city. So, the calculation of the change of LUP and LST in city scale does not show

the accurate image of LST and LUP change of that city. In this study, the identification of LUP and LST

change in the ward basis attempted to solve the problems in large scale LUP and LST change analysis.

Again, the ward wise linear regression analysis between LST and NDVI, LST and NDWI, LST and

NDBI has been conducted for both years. Determination of LST changes based on LUP and surface

temperature change direction based on land use change direction has fulfilled many previous research

limitations.

The rise of LST in Dhaka city has observed 3.20C during the year 2002 to 2014 [18] and in case of

Chittagong metropolitan area it is observed 3.810C [20]. The change of average LST at the research study

area was 3.430C which is found almost same in the case of Dhaka, Chittagong, and Rajshahi City [21].

Due to the implementation of Padma Bridge and various development projects, communication system

with Khulna city and other cities of the country will be developed. This will create new investment

opportunities and therefore, the policy makers hope that the social, physical, and economic condition of

Khulna will improve a lot. As a result, most of the areas of Khulna City are hoping to be turned into

buildup areas. But unplanned urbanization, destruction of eco-friendly areas and high land cover

transformation rate will lead to various environmental problems including the rise of LST and ecological

imbalance in the city. As a result, living in this city will begin to unsuitable day to day. Various problems

will increase including decrease of soil fertility, increase of soil salinity, lowering ground water levels

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etc. which will increase the suffering of the residence by reducing food production, increasing water

crisis, and changing environment. This research will provide information to the people about the effect

of unplanned development and unplanned land cover change on environmental change. Also inspire for

further research on the micro level environmental change due to land use and land cover changes in

Bangladesh.

2.0 METHODOLOGY

2.1 STUDY AREA

The study area was selected as three different wards, Ward No 9 Mujgunni, Ward No 14 Boyra and Ward

No 16 Choto Boyra under Khulna City Corporation area (Figure 1). These three wards are located along

the middle of KCC. In each ward, there are industrial areas, commercial areas along with residential areas.

The total area of the study area is 2113.53 acres with a population of 87430(BBS,2011). About 73% land

use type across the study area is residential and most of these are in ward no 14 & 16. After the

introduction of Mujgunni Residential Area, many buildings are being constructed at ward no 9. That is

why the land cover change has been observed much higher in this area rather than in the surrounding

wards. Although these three study areas are located side by side, but the maximum land use purposes of

these three wards were different. Whereas most of the land cover in Ward No 9 was agricultural land,

residential land in Ward No 16, buildup and open space in Ward No 14. In addition, the percentage of

mixed land use areas in these wards are much higher than other wards of KCC [10,29]. That is why these

three wards have been selected to find the micro level land surface temperature change with the

conversion of land use type.

Figure 1. Location map of study area (KCC Ward No. 9, 14, 16). (Source: KCC, 2019)

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2.2 DATASET PREPARATION

The study has been conducted by using two satellite images of less than 10% land and scene cloud

coverage, collected from US Geological Survey (USGS). Two satellite images of 2001(march) and

2019(march) were taken to avoid the impact of seasonal temperature variations on the land surface

temperature variations in the Landsat images. And due to less possibility of rainfall in Bangladesh during

March, the accuracy or acceptability of LST result is higher than other months. The satellites specification

utilized in the study was described in Table 1.

Table 1. Satellite Sepcification.

Satellite/

Sensor

Pixel

Size

Spectral Resolution Resolution Path and

Row

Date

LANDSAT 5 30 Multispectral (6 Bands) 30 m 138/44 17/03/2001

LANDSAT 8 30 Multispectral (11 Bands) 30 m 137/44 28/03/2019

2.1 LAND USE AND LAND COVER MAP PREPARATION

In order to make land cover map, image enhancement tool is used to classify and select the area of interest.

Contrast Enhancement, Saturation, hue, intensity and Density Slicing etc. are the most used ways to

enhance Landsat images. In this study, Landsat images were enhanced by generating composite band

combination [16]. The radiometric and atmospheric correction of the Landsat satellite images has been

conducted. In this study, the generation of composite band combinations such as natural color composite,

true color composite, false color composite etc. are used to identify the land use type in the study area.

Blue, green, red and near infrared bands were used for Landsat 5 TM images of 2001 and Landsat 8 OLI

images of 2019 to find true color during data processing in Earth Resources Data Analysis System

(ERDAS) Imagine 2014.

The land cover type has been classified into five categories (buildup area, vacant land, agricultural land,

waterbodies, greeneries) in ArcGIS (geo-spatial planning tool) and EARDAS Imagine 14 to identify the

land cover pattern at the study area in the year 2001 and 2019. Then the land cover type change direction

during the study period 2001 to 2019 has been analyzed in ArcGIS 10.6 version.

2.4 ACCURACY ASSESSMENT

During the same time frame, a comparison was made between the 400 random sampling points and their

corresponding point on Google Earth images to verify the LULC classifications [20]. The most likelihood

supervised image classification method used for this research is of good accuracy as the validity rates are

more than 90% for the study area.

2.5 LAND SURFACE TEMPERATURE (LST)

Land surface temperature has been calculated for the year 2001 and 2019 in the study area by using

Landsat 5 TM and Landsat 8 OLI satellite images, respectively. Rahman and Kabir stated the massive

increase of population, industrial areas, and alternation of LCT mostly in the Khulna city since the year

2000 [30] for which the study period is chosen between 2001 to 2019. Then LST map has been generated

for both year and change in LST have been identified for each ward during the study time. Equation 1-5

were used to estimate the LST [21].

Lλ = Aλ + ML ∗ QCAL (1)

where

Lλ = TOA Spectral Radiance (W/ (m2 × sr × μm))

ML = Radiance multiplicative scaling factor for the band

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AL = Radiance additive scaling factor for the band

QCAL = the quantized calibrated pixel value in Digital Numbers (DN)

In the second step the TOA spectral radiance (Lλ) values are converted into another variable called At-

Satellite Brightness Temperature (TB)

TB =k2

lnk1

L1+1

(2)

Where

TB =At-Satellite Brightness Temperature, in Kelvin (K)

K1, K2 = Thermal conversion constants for the band

Finally, the TOA Brightness Temperature will be converting to land surface temperature values (LST)

using the formula.

𝐿𝑆𝑇 = [𝑇𝐵/ (1 + (𝜆 ∗ 𝑇𝐵/ 𝛼)) ∗ 𝑙𝑛 𝜀] (3)

Where

LST = Land Surface Temperature in Kelvin (K)

λ = the wavelength of emitted radiance

α = hc/k (1.438 × 10-2 mK)

h = Planck constant (6.626 × 10-34 J s-1), and c = velocity of light (2.998 × 108 m s-1)

k = Boltzmann constant (1.38 × 10-23 J K-1)

ε = surface emissivity, calculated according to equation 4

𝜀 = 0.004 ∗ 𝑃𝑣 + 0.986 (4)

Where

Pv is the vegetation proportion calculated following equation 5.

𝑃𝑦 = [𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼 𝑚𝑖𝑛

𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼 𝑚𝑎𝑥]

2 (5)

To obtain the land surface temperature values in Celsius (°C), 273.15 was extracted from the initial values

(K).

2.6 NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)

The NDVI is calculated using near-Infrared spectral band and Red spectral band of Landsat’s multi-

spectral sensor. [21]. The concept of estimating the NDVI is based on the absorption of radiation in the

red (R) spectral band and the maximum reflection of radiation in the near-infrared (NIR) spectral band.

𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅 𝐵𝑎𝑛𝑑−𝑅𝐸𝐷 𝐵𝑎𝑛𝑑

𝑁𝐼𝑅 𝐵𝑎𝑛𝑑+𝑅𝐸𝐷 𝐵𝑎𝑛𝑑 (6)

NDVI values vary from −1 to + 1, with values close to −1 suggesting lack of vegetation and values close

to + 1 indicating dense vegetation.

2.7 NORMALIZED DIFFERENCE BUILDUP INDEX (NDBI)

Built-up Index (BU), Normalized Difference Built-up Index (NDBI), Index based Built-up Index (IBI)

etc. are used to analyze the built-up area. In this study, NDBI [12] is used to detect the built-up area in

the study area.

𝑁𝐷𝐵𝐼 = 𝑆𝑊𝐼𝑅−𝑁𝐼𝑅

𝑆𝑊𝐼𝑅+𝑁𝐼𝑅 (7)

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NDBI values vary from −1 to + 1, with values close to −1 suggests no built up area and values close to +

1 indicates the dense built-up area.

2.8 NORMALIZED DIFFERENCE MOISTURE INDEX (NDMI)

NDMI is used for the analysis of water bodies in any area. The index uses remote sensing images with

Green and Near infrared bands. NDMI can efficiently enhance water information in most cases [22]. The

result of NDMI indicates the intensity of waterbodies in the area and illustrates the presence of moisture

in soil [29]. The higher value indicates higher intensity of waterbodies and the lower NDMI value

indicates absence of waterbodies.

𝑁𝐷𝑀𝐼 = 𝑁𝐼𝑅−𝑆𝑊𝐼𝑅

𝑁𝐼𝑅+𝑆𝑊𝐼𝑅 (8)

NDMI values vary from −1 to + 1, with values close to −1 suggests lack of waterbodies and values close

to + 1 indicates presence of large waterbodies.

With the calculated values of NDVI, NDMI and NDBI for both years, the NDVI map, NDMI map and

NDBI map were generated and then the change of these indexes in each ward during the study period has

been identified. Linear regression analysis has been conducted among LST and NDVI, LST and NDWI,

LST and NDBI to see which type of land use pattern is mostly responsible for the change of LST in the

study area.

2.9 RELATIONSHIP BETWEEN LST AND LAND COVER CHANGE

Several Studies [17,18,23,24] have shown that quantifying the LST's effects and spatial patterns involves

a clear understanding of the LST's relationship with different types of land cover. These studies only

explained the change in surface temperature because of the vegetation land cover transformation. In this

study the change of surface temperature due to all types of land cover transformation and their influences

has been analyzed. The study extracted the average, maximum and minimum LST values of the five types

of land cover in the year 2001 and 2019 and identified the average LST change with change rate during

the study period. The study also explored the change of average LST in different land cover, before and

after the land cover transformation. The overall study procedure followed in this study is visualized

through a methodological framework in Figure 2.

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Figure 2. Methodological framework of the study

3.0 ANALYSIS AND INTERPRETATION

3.1 LAND USE AND LAND COVER (LULC) PATTERN CHANGE ANALYSIS

Increase of population and human activities, either on a large scale or small scale are continuously

reducing the waterbody, vegetative cover, vacant land, and agricultural land cover of the earth’s surface.

GIS based satellite image classification in EARDAS Imagine 2014 for the year 2001 and 2019 in Figure

3 shows that the study area has faced a huge amount of land cover transformation in the last 20 years. The

area of each land cover type between 2001 and 2019 for every ward was calculated through maximum

likelihood image classification and presented in Table 3. The positive sign in Table 3 indicates the

increase of land cover percentage while negative sign indicates the decrease of land cover areas. Table 3

shows that there was a drastic and rapid transformation of waterbody at Mujgunni KCC Ward No 9.

Approximately 103.76 acres of waterbodies were transformed into vacant land and buildup area and 229.4

acres of buildup area was increased at Mujgunni. Introduction of Mujgunni Residential Area has

influenced mostly in this rapid increase of the buildup areas at Ward No 9. Table 3 shows the highest

percentage (54.33%) of LULC at ward 9, then 35.65% in ward 14 and 27.44% at ward 16.

Atmospheric & Radiometric

Correction of LANSAT 5 TM &

LANDSAT 8 OLI

LU Classification (Maximum

Likelihood Supervised Image

Classification

Land Use and Land Cover Map

(2001 and 2019)

Land cover change direction

NDMI, NDVI, NDMI

map (2001 and 2019)

Spectral Radiance Model TM

Band for 2001 and Split Window

Algorithm (TIRS) Band 10 & 11

for 2019

Land Surface Temperature Map

(2001 and 2019)

Different Land Surface

Temperature Change

LST dynamics in different land cover conversion

Subdivision of

obtained value into

several ranges

Linear Regression and

Trend Analysis

Identification of

Responsible Land Cover

type for the rise of LST

Measuring LST,

NDMI, NDVI,

NDBI values

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Figure 3. Land Use and Land Cover map of KCC (A) Ward No 9 in 2001 (B) Ward No 9 in 2019 (C) Ward No 14 in 2001

(D) Ward No 14 in 2019 (E) Ward No 16 in 2001 and (F) Ward No 16 in 2019.

Table 3. Land cover area in the year 2001 and 2019 in each ward (Unit: Percentage)

Figure 3C, 3D and Table 3 shows that about 17.83% of buildup area has increased and 5.79% of

vegetation and 1.68% of waterbody area has been decreased at KCC ward no 14. Table 3 illustrates that

buildup area was increased mostly in Ward No 9 by 27.17% and due to the filling of waterbodies and

wetlands, waterbody land cover area percentage was declined by 12.29% during the study period.

Although the rate of Land Use Transformation (LUT) was higher in KCC Ward no 9 and 14, it is noticed

less in case of ward no 16. From the field survey it is found that, due to the establishment of a residential

area in Ward No 9, many houses and building were built in there. As a result, people built many

infrastructures in there for which they filled up waterbodies, wetlands, and agricultural lands. GIS based

satellite image classification in Table 3 and Figure 3E and 3F shows that only 4.73% (28.52 acres) of

buildup area has increased, 0.70% (4.20 acres) of waterbody area and 4.76% (28.7 acres) of vegetated

area was also increased in ward 16. But about 13.72% (72.8 acres) of agricultural land has been decreased

during the study period.

Land Cover

Type

Ward N 9 Ward No. 14 Ward No. 16

2001 2019 Change 2001 2019 Change 2001 2019 Change

Agricultural 14.89% 4.11% -

10.78%

23.17% 13.65% -9.52% 29.02% 15.30% -

13.72%

Build up

Area

7.71% 34.88% 27.17% 27.31% 45.14% 17.83% 29.97% 34.70% 4.73%

Vacant Land 16.18% 12.83% -3.35% 15.52% 14.68% -0.83% 18.44% 21.99% 3.54%

Vegetation 36.56% 35.83% -0.74% 31.20% 25.41% -5.79% 21.65% 26.40% 4.76%

Waterbody 24.65% 12.37% -

12.29%

2.80% 1.13% -1.68% 0.92% 1.61% 0.70%

Total 100% 100% 54.33% 100% 100% 35.65% 100% 100% 27.44%

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The land use and land cover (LULC) change direction analysis in ArcGIS shows a remarkable change of

LULC in 20 years. Figure 4 shows that about 110.73acres (5.24%) of waterbodies, 227.27acres (10.75%)

of agricultural land has been transformed into build up areas. The land cover change direction analysis

illustrated in Figure 4 shows that waterbody and agricultural land cover were worst affected in the study

area. Due to the high percentage of agricultural and waterbody land cover area in the study area, these

two land cover types have become transformed mostly. Moreover, many waterbodies are still being filled

for further construction of buildings in the future. As a result of urban expansion and growth of population,

vegetation, waterbodies, agricultural land, and vacant land surfaces exposed.

Figure 4. LULC change direction analysis between 2001 to 2019 at study area.

3.2 LST DYNAMICS IN RESPONSE TO NDMI

The value of NDMI lies between -1 to 0 to +1. Higher value NDMI indicates the higher density of water

surface and lower density of buildup area. Using equation 8, NDMI profile of the study area in both study

period was calculated and presented in Figure 5.

Figure 5. Normalized Difference Moisture Index map of KCC (A) Ward No 9 in 2001 (B) Ward No 9 in 2019 (C) Ward No

14 in 2001 (D) Ward No 14 in 2019 (E) Ward No 16 in 2001 and (F) Ward No 16 in 2019.

-5.24%

-0.76%

-0.59%

-10.75%

17.82%

Waterbody

Vegetation

Vacant Land

Agricultural land

Buildup Area

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With the growth of population and because of urbanization, 5.24% (110.73 acres) of waterbodies (Figure

4) has been transformed across the study and resulted in the decrease of NDMI value (Figure 5). The

higher value of NDMI is decreased from 0.43 to 0.33 in 9 No Ward (Figure 5A, B) and increased from

0.29 to 0.27 in 16 No Ward (Figure 5E, F). But the percentage of dense water cover areas across the study

area has reduced.

As in Ward No 16, vegetation and waterbodies area has increased (4.76% and 0.70% respectively in Table

3) and buildup area increasing rate was low, the total percentage of areas with higher NDMI value was

increased in 2019 (Figure 5E, 5F). Though the higher value of NDMI at 14 No Ward has increased but

the percentage of buildup area has increased (Figure 2C, D). Therefore, it can be concluded that NDMI

has been decreasing and the percentage of areas with lower NDMI value are increasing day by day across

the study area.

Figure 6. Linear correlation between LST change in response to NDMI value change in the year (A) 2001 and (B) 2019.

From the regression analysis between LST and NDMI in Figure 6 shows an increasing trend of LST with

the decrease of NDMI value. The regression analysis for the year 2001 in Figure 6 represents the negative

relationship between LST and NDMI values i.e. decrease of waterbodies influences in the increase of

surface temperature. The value of coefficient of determination (R2) was calculated 0.9976 for the year

2001 and 0.9909 for the year 2019 which indicates the significant relationship between NDMI and LST.

Figure 6 states that the surface temperature increased during the study period because of the reduction of

the total amount of waterbodies in the study area.

3.3 LST DYNAMICS IN RESPONSE TO NDVI

The values of NDVI vary between -1 to +1. The values of NDVI more than 0.3 indicates the healthy

vegetation, the value from 0.01 to 0.02 and from 0.02 to 0.03 indicates non vegetated area and unhealthy

vegetation [29]. The NDVI values for the study area was calculated by using equation 6 and illustrated in

Figure 7. Figure 7 visualizes the declination of the healthy vegetation and increase of the unhealthy

vegetation area in every ward. NDVI change analysis shows that the percentage of the unhealthy

vegetation area and non-vegetated area has been increased at Mujgunni mostly. Where the total amount

of healthy vegetation areas declined mostly at ward no 14 showed in Figure 7C, 7D. That is why the

average LST increased mostly at ward no 9 and ward no 14 mostly which presented in Figure 7.

y = -19.177x + 23.39

R² = 0.9976

15

17

19

21

23

25

27

29

-0.3 -0.2 -0.1 0 0.1 0.2 0.3

LS

T (

0C

)

NDMI

a

y = -10.027x + 27.321

R² = 0.9909

20

22

24

26

28

30

32

-0.4 -0.2 0 0.2 0.4 0.6

LS

T

(0C

)

NDMI

b

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Figure 7. Normalized Difference Vegetation Index map of KCC (A) Ward No 9 in 2001 (B) Ward No 9 in 2019 (C) Ward

No 14 in 2001 (D) Ward No 14 in 2019 (E) Ward No 16 in 2001 and (F) Ward No 16 in 2019.

In 2001, NDVI range was between -0.0082 to +0.48, -0.0034 to +0.44 and -0.024-0.43 at ward no 9, 14

and 16, respectively. But the value has been changed and showed a positive change condition in 2019

though, the average NDVI value declined from 0.25 to 0.1837. Transformation of vegetation areas and

agricultural land is responsible for the increase of LST in the study area. If the deforestation and cut of

urban trees are not stopped, then this situation will continue to be worse day by day.

The linear regression trend analysis of NDVI (Figure 8) for the year 2001 and 2019 shows the strong

negative correlation between LST and NDVI value. Figure 8 indicates that surface temperature decreases

with the increase of NDVI values that means with the increase of dense vegetation areas. The coefficient

of determination value found 0.986 for the year 2001 and 0.9955 for the year 2019 and both indicates the

strong or significant response of surface temperature with the change of NDVI values.

Figure 8. Linear correlation between LST change in response to NDVI value change in the year (A) 2001 and (B) 2019.

(Source: Author, 2020)

y = -11.658x + 29.465

R² = 0.986

22

24

26

28

30

32

-0.2 0 0.2 0.4 0.6 0.8

LS

T (

0C

)

NDVI

a

y = -21.172x + 28.77

R² = 0.9955

18

20

22

24

26

28

30

0 0.1 0.2 0.3 0.4 0.5

LS

T

(0C

)

NDVI

b

105

3.4 LST DYNAMICS IN RESPONSE TO NDBI

Increase of buildup areas by 376.59 acres over the study period has increased the NDBI value across the

study area. NDBI have indices value ranges from -1 to 1. The higher value of NDBI has increased from

0.056 to 0.43 at ward no 9 (Figure 9A, B), 0.10 to 0.33 at ward no 14 (Figure 9 C, D) and 0.14 to 0.30 at

ward no 16 (Figure 9 E, F). The analysis shows that NDBI has increased 8 times at ward no 9, 3 times at

ward 14 and 2 times at ward 16.

Figure 9 Normalized Difference Buildup Index map of KCC (A) Ward No 9 in 2001 (B) Ward No 9 in 2019 (C) Ward No 14

in 2001 (D) Ward No 14 in 2019 (E) Ward No 16 in 2001 and (F) Ward No 16 in 2019.

Figure 9 visualizes a huge change of NDBI during the year 2001 to 2019 across the study area.

Figure 10. Linear correlation between LST change in response to NDBI value change in the year (A) 2001 and (B) 2019.

y = 9.8639x + 26.975

R² = 0.9867

22

24

26

28

30

32

-0.4 -0.2 0 0.2 0.4 0.6

LS

T (

0C

)

NDBI

a

y = 19.25x + 23.021

R² = 0.9979

18

20

22

24

26

28

30

-0.2 -0.1 0 0.1 0.2 0.3

LS

T (

0C

)

NDBI

b

106

As the total amount of increased buildup area was most at Mujgunni, the value of NDBI is changed mostly

in this area than other wards. In 2001 the NDBI range was between -0.35 and 0.14 across the study area,

which is gradually increased to between -0.43 to + 0.43 (Figure 9). The higher value increased about 3

times and the lower value of NDBI has decreased very slightly. Therefore, it can be concluded that the

NDBI is increasing at a tremendous rate in KCC areas over the time.

The linear regression between LST and NDBI and the trend analysis in Figure 10 represents the rise of

LST with the increase of NDBI value over the time. The value of coefficient of determination, R2= 0.9867

in 2001(Figure 10 A) describes the strong responsive relationship between LST and NDBI. The

transformation of other land cover types in buildup areas has influenced to make the relationship stronger

in the year 2019. The value of R2= 0.9979 in the year 2019 (Figure 10 B) indicates the strongly significant

relationship between LST and NDBI. The coefficient of determination in the Figure 6, 8 and 10 suggests

that LST responded mostly with the change of NDBI value (as the relationship between NDBI and LST

found most significant) i.e. the increase of buildup area is mostly responsible for the increase of surface

temperature in the study area during the study time period.

3.5 LAND SURFACE TEMPERATURE(LST) CHANGE

The surface temperature change analysis shows that the average land surface temperature across the study

area has increased by 3.430C during the study period at the rate of 1.7140C/decade. Table 4 shows the

increase of the minimum, maximum and average temperature of different land cover during the study

period. The derived LST value range for each ward during the study period illustrated in Figure 11 shows

the clear increase of LST of each ward. The lowest LST increased by 1.710C, 1.850C and 0.740C at Ward

No 9, 14 and 16, respectively. The reason for this higher increase of LST value at ward 9 and 14 is that

the buildup area has increased mostly at these wards. LULC change direction analysis in Figure 4

demonstrated the declination of a huge percentage of ecofriendly land cover types such as agricultural

land, waterbodies, and vegetation land cover areas during the study period. This has adversely affected

the environment of the study area and influenced in the alternation of surface heat retention capacity. As

the buildup area was increased and the total amount of ecofriendly land cover types was decreased, the

percentage of area with higher LST value has increased. Such increase of surface temperature will reduce

the fertility of agricultural land and will increase soil salinity of the study area.

Table 4. Observed LST in different land cover in the year 2001 and 2019

Land use

LST in 2001 (OC) LST in 2019 (OC)

Minimum Maximum Average Minimum Maximum Average

Buildup Area 25.40 27.52 26.24 28.35 31.24 30.83

Vacant Land 24.55 26.67 25.80 26.19 30.42 29.95

Agricultural Land 23.68 27.10 24.85 25.45 28.18 27.39

Vegetation Land 23.68 26.67 24.83 25.83 30.01 28.23

Waterbody 23.68 27.93 26.16 25.40 30.42 28.88

The derived LST value range (higher value and lower value) in Figure 11A, 11C, 11D shows that the

range of observed LST in each ward was different in 2001 and the variation is also remained in the year

2019. As, the total amount of vegetation and waterbody areas has increased at ward 16 (Table 3), the

higher value of LST in this ward has decreased (28.760C to 28.700C) (Figure 11 E, F). The average LST

increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to

27.120C in Ward No 16. The total amount of areas with higher LST value has increased mostly at ward

no 9 (Figure 11 C, D) and at ward 16 (Figure 11 E, F). Though the distance between these wards is very

less but the range of LST value varied in these three wards. Figure 3 and Figure 11 reveals that LST has

responded variedly in each ward as the LULC change also varied in each ward.

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Figure 11. Land Surface Temperature map of KCC (A) Ward No 9 in 2001 (B) Ward No 9 in 2019 (C) Ward No 14 in 2001

(D) Ward No 14 in 2019 (E) Ward No 16 in 2001 and (F) Ward No 16 in 2019.

Table 5 shows that average LST of buildup areas increased mostly by 4.590C and the average LST of

vacant land, waterbody and agricultural land cover increased from 25.800C to 29.950C, 26.160C to

28.230C and 24.850C to 27.390C respectively (Table 4). Destruction of waterbodies and the rise of

atmospheric temperature has influenced in the increase of water surface temperature. The rise of

minimum, maximum and average LST in buildup areas land cover during the study time period and

regression analysis between NDVI and LST, NDMI and LST, NDBI and LST demonstrated that increase

of buildup area is mostly responsible for the increase of Land Surface Temperature in the study area.

Table 5. Nature of average surface temperature increase in different land cover during the study period

Land Cover Type Increased LST (0C) Avg. LST increasing rate (0C/year)

Buildup Area 4.59 0.229

Vacant Land 4.15 0.2075

Agricultural Land 2.54 0.127

Vegetation Land 3.4 0.17

Waterbody 2.72 0.136

3.6 LST DYNAMICS IN RESPONSE TO LULC TRANSFORMATION

Surface temperature changes during the study year in different land cover types was determined across

the study area. Increase of LST has been observed in all types of land cover transformation except

transformation of LC into waterbodies. Surface temperature has increased mostly due to the increase in

buildup areas (Table 6). Conversion of waterbody into build up area has increased surface temperature

mostly by 3.230C with a rate of 0.1610C/year (Table 6).

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Table 6. Surface temperature change in response to land cover change in the study area.

Land Cover

Transformation

Avg. Temp in

2001(0C)

Avg. Temp in

2019(0C)

Avg. LST

Change (0C)

Increasing Rate

(0C/year)

Waterbody to Buildup 25.91 29.14 3.23 0.161

Waterbody to Vacant 24.83 27.88 3.05 0.153

Waterbody to Agricultural 25.19 27.01 1.82 0.091

Vegetation to Buildup 26.67 28.95 2.28 0.114

Vegetation to Agricultural 24.37 26.49 2.12 0.106

Vegetation to Vacant 25.57 27.18 1.61 0.081

Agricultural to Buildup 27.03 30.06 3.03 0.151

Agricultural to Vacant 24.54 27.40 2.84 0.142

Agricultural to Waterbody 26.01 25.55 -0.46 -0.023

Vacant to Buildup 26.89 29.93 3.04 0.152

Vacant to waterbody 26.23 25.84 -0.39 -0.020

Vacant to Agricultural 25.88 27.91 2.03 0.102

The analysis shows that the surface temperature has changed mostly due to the conversion of vegetation,

agricultural land and waterbody into buildup areas which is also found in the NDBI and NDMI value

change direction analysis and their trend analysis. Transformation of vacant land and agricultural land

into waterbodies has decreased the surface temperature by 0.390C and 0.460C, respectively. The analysis

shows that the surface temperature has risen in response to various changes in land use and land cover in

different areas of the study area. The overall land cover transformation rate across the study area is

indicating a huge increase of surface temperature in the future.

4.0 CONCLUSION

The study reveals a drastic land use and land cover transformation of agricultural land and waterbodies

in the study area. Unplanned urbanization has resulted in the increase of 376.59acres (17.82%) of buildup

area and destruction of 227.27 acres (10.75%) of agricultural land and 110.73 acres (5.24%) of

waterbodies in the study area in just 18 years. This high increasing rate of buildup area has influenced in

the unexpected change of NDMI, NDVI and NDBI indices values. Though the three wards are located

side by side, but the LST and NDMI, NDVI, NDBI indices values have changed differently as the land

cover transformation type varied in each ward. The Most distinct observation was that the surface

temperature has increased mostly in those areas where waterbodies and vacant land has transformed into

buildup areas. Overall study demonstrates that the land covers are not changed in a planned way in

everywhere in Khulna city. As a result, environmental changes in microlevel are taking place very subtly

such as the rapid increase of surface temperature, atmospheric temperature, and changes in landscape.

This unplanned land cover type conversion into buildup areas has increased the average LST by 3.430C

with the rate of 1.7140C/decade across the study area. Such land cover change at micro level are causing

many other adverse reactions to its environment, which in the macro level such as city level is often not

well realized. The valuation of environmental change at the micro level through this study will help to

aware people about the significance of planned land use change and researcher to focus on the root level

land use dynamics to mitigate the adverse effects of it on the environment.

5.0 ACKNOWLEDGEMENT

The authors gratefully acknowledged the EarthExplorer -USGS as the source of satellite images and also

thankful to Professor Mustafa Saroar, KUET for giving the theoretical knowledge. Mr. Saif Siam, Mr.

Fazle Rabbi and Fahmida Yeasmin Sami helped in data collection. Anonymous reviewers are also

109

acknowledged for their crucial comments that have helped the authors to improve the manuscript

substantially. The authors are also grateful to the editorial team.

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